Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents
Advancing agent adaptability through dynamic, adversarial training loops for complex social simulations.
Role-playing agents based on large language models have been widely applied in areas such as personalized assistance and social simulation. Recent RL methods typically train on a fixed scenario pool collected before learning begins. This creates a distributional bottleneck: as th...
ArXiv CS.AI
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This research breaks the static training bottleneck by implementing an adversarial loop that continuously generates challenging scenarios for role-playing agents. By forcing agents to adapt to evolving complexity, the model achieves significantly higher robustness and more sophisticated conversational dynamics.
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